SoroushVahidi/scidocs huggingface dataset:
Human C-tactile afferents are tuned to the temperature of a skin-stroking caress.
(2014)

Human C-tactile (CT) afferents respond vigorously to gentle skin stroking and have gained attention for their importance in social touch. Pharmacogenetic activation of the mouse CT equivalent has positively reinforcing, anxiolytic effects, suggesting a role in grooming and affiliative behavior. We recorded from single CT axons in human participants, using the technique of microneurography, and stimulated a unit's receptive field using a novel, computer-controlled moving probe, which stroked the skin of the forearm over five velocities (0.3, 1, 3, 10, and 30 cm s(-1)) at three temperatures (cool, 18 °C; neutral, 32 °C; warm, 42 °C). We show that CTs are unique among mechanoreceptive afferents: they discharged preferentially to slowly moving stimuli at a neutral (typical skin) temperature, rather than at the cooler or warmer stimulus temperatures. In contrast, myelinated hair mechanoreceptive afferents proportionally increased their firing frequency with stroking velocity and showed no temperature modulation. Furthermore, the CT firing frequency correlated with hedonic ratings to the same mechano-thermal stimulus only at the neutral stimulus temperature, where the stimuli were felt as pleasant at higher firing rates. We conclude that CT afferents are tuned to respond to tactile stimuli with the specific characteristics of a gentle caress delivered at typical skin temperature. This provides a peripheral mechanism for signaling pleasant skin-to-skin contact in humans, which promotes interpersonal touch and affiliative behavior.
Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using PID controller
(2016)

Recently, Multisensor Fire Detection System (MSFDS) is one of the important research issues. Here, a fire fighter robot is fabricated providing extinguishment platform. The base of the robot is made of the wood of `Rashed tree', locally known as `Kerosene wood'. There is about 1 liter water reserving capacity. An arduino based simple algorithm is used for detection of fire and measurement of distance from fire source while the robot is on its way to extinguish fire. When the fire is detected and the robot is at a distance near to fire, a centrifugal pump is used to throw water for extinguishment purpose. A water spreader is used for effective extinguishing. It is seen that velocity of water is greatly reduced due to the use of water spreader. Two sensors: LM35 and Arduino Flame Sensors are used to detect the fire and distances on its way towards fire. Sensitivity of these sensors at different day times and distances is tested through analog reading of the serial monitor.
Towards Discipline-Independent Argumentative Zoning : Evidence from Chemistry and Computational Linguistics
(2009)

Argumentative Zoning (AZ) is an analysis of the argumentative and rhetorical structure of a scientific paper. It has been shown to be reliably used by independent human coders, and has proven useful for various information access tasks. Annotation experiments have however so far been restricted to one discipline, computational linguistics (CL). Here, we present a more informative AZ scheme with 15 categories in place of the original 7, and show that it can be applied to the life sciences as well as to CL. We use a domain expert to encode basic knowledge about the subject (such as terminology and domain specific rules for individual categories) as part of the annotation guidelines. Our results show that non-expert human coders can then use these guidelines to reliably annotate this scheme in two domains, chemistry and computational linguistics.
Fitting conic sections to "very scattered" data: An iterative refinement of the bookstein algorithm
(1982)

An examination of the geometric interpretation of the error-of-fit measure of the Bookstein algorithm for fitting conic sections shows why it may not be entirely satisfactory when the data are “very scattered” in the sense that the data points are distributed rather widely about an underlying smooth curve. A simple iterative refinement of the Bookstein algorithm, similar in spirit to iterative weighted least-squares methods in regression analysis, results in a fitted conic section that approximates the conic that would minimize the sum of squared orthogonal distances of data points from the fitted conic. The usefulness and limitations of the refined algorithm are demonstrated on two different types of “very scattered” data.
The Taste for Privacy: An Analysis of College Student Privacy Settings in an Online Social Network
(2008)

The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by a unique set of cultural preferences—of which the ‘‘taste for privacy’’ may be only a small but integral part.
A face(book) in the crowd: social Searching vs. social browsing
(2006)

Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.
Instant messaging in teen life
(2002)

Instant Messaging (IM) is being widely adopted by teenagers. In a study of 16 teenage IM users, we explore IM as an emerging feature of teen life, focusing our questions on its support of interpersonal communication and its role and salience in everyday life. We qualitatively describe the teens' IM use interpersonally, as well as its place in the domestic ecology. We also identify technology adoption conditions and discuss behaviors around privacy management. In this initial investigation, we found differences in the nature of use between high school and college teens, differences we propose are accounted for by teens' degree of autonomy as a function of domestic and scholastic obligations, the development of independent work practices, Internet connectivity access, and even transportation access. Moreover, while teen IM use is in part characterized as an optimizing choice between multiple communications media, practice is also tied to concerns around peer pressure, peer group membership and creating additional opportunities to socialize.
The lexical constituency model: some implications of research on Chinese for general theories of reading.
(2005)

The authors examine the implications of research on Chinese for theories of reading and propose the lexical constituency model as a general framework for word reading across writing systems. Word identities are defined by 3 interlinked constituents (orthographic, phonological, and semantic). The implemented model simulates the time course of graphic, phonological, and semantic priming effects, including immediate graphic facilitation followed by graphic inhibition with simultaneous phonological facilitation, a pattern unique to the Chinese writing system. Pseudocharacter primes produced only facilitation, supporting the model's assumption that lexical thresholds determine phonological and semantic, but not graphic, effects. More generally, both universal reading processes and writing system constraints exist. Although phonology is universal, its activation process depends on how the writing system structures graphic units.
Statistics of Patch Offsets for Image Completion
(2012)

Image completion involves filling missing parts in images. In this paper we address this problem through the statistics of patch offsets. We observe that if we match similar patches in the image and obtain their offsets (relative positions), the statistics of these offsets are sparsely distributed. We further observe that a few dominant offsets provide reliable information for completing the image. With these offsets we fill the missing region by combining a stack of shifted images via optimization. A variety of experiments show that our method yields generally better results and is faster than existing state-of-the-art methods.
Phase-Error Measurement and Compensation in PLL Frequency Synthesizers for FMCW Sensors—I: Context and Application
(2007)

The synthesis of linear frequency sweeps or chirps is required, among others, in frequency-modulated continuous-wave radar systems for object position estimation. Low phase and frequency errors in sweeps with high bandwidth are a prerequisite for good accuracy and resolution, but, in certain applications where high measurement rates are desired, the additional demand for short sweep cycles has to be met. Transient phenomena in dynamic synthesizers as well as nonlinear system behavior usually cause unknown phase errors in the system output. For the class of phase-locked-loop (PLL)-based frequency synthesizers, a novel output phase-measurement method and dedicated circuitry are proposed that allow significant reduction of phase errors by adaptive input predistortion. The measurement procedure is implemented within the PLL control circuitry and does not require external equipment. The application of this method to PLL system identification and linearization of extremely short frequency sweeps is shown
Advertisement image classification using convolutional neural network
(2017)

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, plant (fruits, flowers, leaf…) classification base on images. This paper proposes the image classification model applied for identifying the display of the online advertisement. The proposal model uses Convolutional Neural Network with two parameters (n, m) where n is a number of layers and m is number of filters in Conv layer. The proposed model is called nLmF-CNN. The suitable values of parameters (n, m) for advertisement image classification are identified by experiments. The input data of the proposed model are online captured images. The processing components of nLmF-CNN are developed as deep neural networks using ConvNetJs library. The output of the proposed model is YES/NO. YES means that the advertisements display clearly. NO means that the advertisements do not display or not clear. The experimental results 86% in our normalizing dataset showed the feasibility of a proposed model nLmF-CNN.
Large Scale Cloudlets Deployment for Efficient Mobile Cloud Computing
(2015)

Interest in using Mobile Cloud Computing (MCC) for compute intensive mobile devices jobs such as multimedia applications has been growing. In fact, many new research efforts were invested to maximize the mobile intensive jobs offloading to the cloud within the cloud system coverage area. However, a large scale MCC deployment challenges and limitations are not considered. In this paper, a large scale Cloudlet-based MCC system deployment is introduced, aiming at reducing the power consumption and the network delay of multimedia applications while using MCC. The proposed deployment offers a large coverage area that can be used by mobile devices while they are moving from one location to another to reduce broadband communication needs. Practical experimental results and simulated results using show that using the proposed model reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device requests a job to be performed remotely while satisfying the high quality of service requirements of mobile users.
Auditory Saliency Using Natural Statistics
(2012)

In contrast to the wealth of saliency models in the vision literature, there is a relative paucity of models exploring auditory saliency. In this work, we integrate the approaches of (Kayser, Petkov, Lippert, & Logothetis, 2005) and (Zhang, Tong, Marks, Shan, & Cottrell, 2008) and propose a model of auditory saliency. The model combines the statistics of natural soundscapes and the recent past of the input signal to predict the saliency of an auditory stimulus in the frequency domain. To evaluate the model output, a simple behavioral experiment was performed. Results show the auditory saliency maps calculated by the model to be in excellent accord with human judgments of saliency.
Synthesizing data for text recognition with style transfer
(2018)

Most of the existing datasets for scene text recognition merely consist of a few thousand training samples with a very limited vocabulary, which cannot meet the requirement of the state-of-the-art deep learning based text recognition methods. Meanwhile, although the synthetic datasets (e.g., SynthText90k) usually contain millions of samples, they cannot fit the data distribution of the small target datasets in natural scenes completely. To address these problems, we propose a word data generating method called SynthText-Transfer, which is capable of emulating the distribution of the target dataset. SynthText-Transfer uses a style transfer method to generate samples with arbitray text content, which preserve the texture of the reference sample in the target dataset. The generated images are not only visibly similar with real images, but also capable of improving the accuracy of the state-of-the-art text recognition methods, especially for the English and Chinese dataset with a large alphabet (in which many characters only appear in few samples, making it hard to learn for sequence models). Moreover, the proposed method is fast and flexible, with a competitive speed among common style transfer methods.
DRC: a dual route cascaded model of visual word recognition and reading aloud.
(2001)

This article describes the Dual Route Cascaded (DRC) model, a computational model of visual word recognition and reading aloud. The DRC is a computational realization of the dual-route theory of reading, and is the only computational model of reading that can perform the 2 tasks most commonly used to study reading: lexical decision and reading aloud. For both tasks, the authors show that a wide variety of variables that influence human latencies influence the DRC model's latencies in exactly the same way. The DRC model simulates a number of such effects that other computational models of reading do not, but there appear to be no effects that any other current computational model of reading can simulate but that the DRC model cannot. The authors conclude that the DRC model is the most successful of the existing computational models of reading.
DF2Net: Discriminative Feature Learning and Fusion Network for RGB-D Indoor Scene Classification
(2018)

This paper focuses on the task of RGB-D indoor scene classification. It is a very challenging task due to two folds. 1) Learning robust representation for indoor scene is difficult because of various objects and layouts. 2) Fusing the complementary cues in RGB and Depth is nontrivial since there are large semantic gaps between the two modalities. Most existing works learn representation for classification by training a deep network with softmax loss and fuse the two modalities by simply concatenating the features of them. However, these pipelines do not explicitly consider intra-class and interclass similarity as well as inter-modal intrinsic relationships. To address these problems, this paper proposes a Discriminative Feature Learning and Fusion Network (DFNet) with two-stage training. In the first stage, to better represent scene in each modality, a deep multi-task network is constructed to simultaneously minimize the structured loss and the softmax loss. In the second stage, we design a novel discriminative fusion network which is able to learn correlative features of multiple modalities and distinctive features of each modality. Extensive analysis and experiments on SUN RGB-D Dataset and NYU Depth Dataset V2 show the superiority of DFNet over other state-of-the-art methods in RGB-D indoor scene classification task.
Semi-supervised auto-encoder based on manifold learning
(2016)

Auto-encoder is a popular representation learning technique which can capture the generative model of data via a encoding and decoding procedure typically driven by reconstruction errors in an unsupervised way. In this paper, we propose a semi-supervised manifold learning based auto-encoder (named semAE). semAE is based on a regularized auto-encoder framework which leverages semi-supervised manifold learning to impose regularization based on the encoded representation. Our proposed approach suits more practical scenarios in which a small number of labeled data are available in addition to a large number of unlabeled data. Experiments are conducted on several well-known benchmarking datasets to validate the efficacy of semAE from the aspects of both representation and classification. The comparisons to state-of-the-art representation learning methods on classification performance in semi-supervised settings demonstrate the superiority of our approach.
Web-scale knowledge extraction from semi-structured tables
(2010)

A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tables and attribute/value tables. We report the frequencies of these table types over a large analysis of the Web and propose open challenges for extracting from attribute/value tables semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present in the table itself. In 79% of our Web tables, our method finds the correct protagonist in its top three returned candidates.
Teaching Virtualization by Building a Hypervisor
(2015)

Virtual machines (VMs) are an increasingly ubiquitous feature of modern computing, yet the interested student or professional has limited resources to learn how VMs work. In particular, there is a lack of "hands-on" exercises in constructing a virtual machine monitor (VMM, or hypervisor), which are both simple enough to understand completely but realistic enough to capture the practical challenges in using this technology. This paper describes a set of assignments to extend a small, pedagogical operating system (OS) to form a hypervisor and host itself. This pedagogical hypervisor, called HOSS, adds roughly 1,000 lines of code to the MIT JOS source, and includes a set of guided exercises. Initial results with HOSS in an upper-level virtualization course indicate that students enjoyed the assignments and were able to apply what they learned to solve different virtualization-related problems. HOSS is publicly available.
Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns
(2012)

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps, in the first step K-Means clustering technique is used for the image segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of apple fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.
Inspired by distraction: mind wandering facilitates creative incubation.
(2012)

Although anecdotes that creative thoughts often arise when one is engaged in an unrelated train of thought date back thousands of years, empirical research has not yet investigated this potentially critical source of inspiration. We used an incubation paradigm to assess whether performance on validated creativity problems (the Unusual Uses Task, or UUT) can be facilitated by engaging in either a demanding task or an undemanding task that maximizes mind wandering. Compared with engaging in a demanding task, rest, or no break, engaging in an undemanding task during an incubation period led to substantial improvements in performance on previously encountered problems. Critically, the context that improved performance after the incubation period was associated with higher levels of mind wandering but not with a greater number of explicitly directed thoughts about the UUT. These data suggest that engaging in simple external tasks that allow the mind to wander may facilitate creative problem solving.
Design optimization of interior permanent magnet (IPM) motors with maximized torque output in the entire speed range
(2005)

A multiobjective optimization procedure for the design of interior PM motors based on differential evolution algorithm is presented. A comparative analysis has been carried out for the optimized 5 kW, 50 kW and 200 kW IPM motors with two and three layers of cavities in the rotor to show design trade-offs between goals to minimize the motor volume while maximizing the power output in the field weakening regime with the back emf constraint as the main limiting factor in the design. The finite element method has been used to calculate the motor parameters during the optimization process. The effectiveness of the proposed design methodology has been shown on a 1.65 kW IPM motor with two layers of cavities in the rotor for which a prototype has been built and tested
Camera Tracking for Augmented Reality Media
(2000)

This paper presents a camera tracking system for the spatial stabilization of Augmented Reality (AR) media. Our approach integrates both artificial landmarks (fiducials) and natural features for camera tracking. Artificial landmarks are used for system initialization and computation of initial camera pose. Robust and extendible tracking is achieved by dynamically calibrating the 3D positions of a priori uncalibrated natural features. Analysis and experimental results demonstrate the effectiveness of this approach for presenting stabilized AR media in long camera-motion video sequences.